International journal of industrial engineering and operational research

Identifying and Ranking the Factors Affecting the Brand Experience of Public Organizations (A Case Study of Social Security Organization)

2024-07-28T18:56:38+0330

This paper investigates the factors influencing the brand experience of public organizations, focusing on the Social Security Organization (SSO). Employing Multi-Criteria Decision Making (MCDM) methods, specifically the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this study identifies and ranks the key factors affecting brand experience. The findings offer valuable insights for policymakers and managers in public organizations to enhance their brand experience effectively.

Impact of Image Processing on Process Improvement in a Manufacturing Industry

2024-07-31T11:01:43+0330

Image data has a significant impact on streamlining production processes by representing visual information, thereby improving overall organizational efficiency. Extracting valuable insights from image data is crucial for monitoring and enhancing statistical processes in manufacturing industries. This study introduces a new method based on image processing and fuzzy transform approach to process improvement in the manufacturing industry. The information is monitored using an exponentially weighted moving average chart (EWMA) control chart to detect change points. Furthermore, a real case study in the tile manufacturing industry, along with various numerical examples, is examined under different scenarios to assess the effectiveness of image processing on process improvement with the proposed method. The results of experimental tests show promising performance for the proposed approach.

Project Portfolio Selection in the Construction Industry by Optimization Approach

2024-08-08T17:51:00+0330

In the construction industry, Project Portfolio Selection (PPS) is crucial for enhancing resource allocation, maximizing returns, and minimizing risks. This paper presents an in-depth analysis of PPS using optimization techniques. By leveraging mathematical models and decision-making frameworks, we emphasize the significance of optimization in achieving strategic objectives. The study reviews existing literature, identifies key factors influencing project selection, and proposes a comprehensive methodology that integrates quantitative and qualitative criteria. Numerical simulations demonstrate the effectiveness of the proposed approach. The findings indicate that utilizing optimization can significantly improve project outcomes and align them with organizational goals.

Multi-objective Design of a Blood Supply Chain Based on Sustainability Approach and Demand Prediction Using Deep Learning Algorithm

2024-09-08T09:08:15+0330

One of the most critical components of a healthcare system is the blood supply chain, which accounts for a significant proportion of the system's expenditure. Therefore, any improvement in the blood supply chain's performance can significantly increase healthcare systems' efficiency and cost-effectiveness. The main challenge in managing blood products lies in supply and demand uncertainty, leading to a trade-off between scarcity and waste, especially in developing countries. In addition, the predictive power of deep learning models for estimating and forecasting the demand for blood products has yet to be sufficiently explored. This paper proposes a multi-objective model to optimize the blood supply chain network. The objectives include minimizing blood delivery times, reducing economic costs in the supply chain, reducing carbon dioxide emissions, and maximizing demand satisfaction as an aspect of social sustainability. Given the uncertainty of blood supply and demand, a deep learning model based on the CNN method is used to predict blood demand. The LP-Metric algorithm is used to solve the model in the GAMS software, and the SA simulation algorithm is used to validate the results. The calculation results show that the SA algorithm performs better in optimizing the first objective function, resulting in a shorter product delivery time. However, the LP-Metric method performs better for the second and third objective functions.